Stanford HAI Welcomes SAP to the Corporate Affiliate Program
The Stanford Institute for Human-Centered AI is pleased to announce that German multinational software company SAP SE has joined as a member of the Stanford HAI Corporate Affiliate Program.
The program brings industry together with Stanford faculty to explore research, policy and education opportunities around responsibly building and implementing artificial intelligence.
As a member of the HAI corporate affiliate program, SAP, which develops enterprise software to manage business operations and customer relations, will support new research areas in foundation models and generative AI, participate in forums, and engage with students.
“Stanford HAI has played a pivotal role in accelerating advancements in foundation models, particularly in the realm of AI safety and the consideration of this new technology's societal impacts,” said James Landay, vice director and director of research for Stanford HAI. “We look forward to collaborating with SAP as a corporate affiliate member as we aim to unlock novel use cases and develop new research contributions in response.”
“We're steadfast in our mission to enhance the human-centeredness of AI. To truly make a difference, the scale of impact is paramount.,” said Panos Madamopoulos-Moraris, Stanford HAI’s managing director for industry programs and partnerships. “We're ecstatic to embark on this collaboration with SAP as a corporate affiliate member, magnifying our capacity to democratize access to human-centric AI technologies.”
“Generative AI offers a whole new world of opportunities to simplify and improve our lives. As the AI landscape continues to evolve rapidly, SAP focuses on building AI into core business processes to drive productivity and growth for companies worldwide,” said Juergen Mueller, chief technology officer and executive board member at SAP. “Joining the Stanford HAI corporate affiliate program reinforces our strong commitment to building and using relevant, reliable and responsible Business AI while providing us with access to the latest research and allowing us to engage with talents and experts in the field.“
“This collaboration between Stanford HAI and SAP is not just about advancing technology, it's about defining new ways of using AI to drive better business outcomes for society and businesses,” said Walter Sun, senior vice president and global head of AI, SAP. “We are excited to support HAI’s efforts on trustworthy AI, which includes grounding of large language models (LLMs) on verifiable knowledge using vector embeddings along with new innovations on fine-tuning. Additionally, we look forward to engaging with HAI’s Center for Research on Foundation Models (CRFM).”
The HAI Corporate Affiliate Program engages with companies that share HAI’s mission to advance AI research, education, policy, and practice to improve the human condition. The program provides the opportunity for these companies to interact with Stanford faculty as well as other corporate members, through educational experiences, lectures, research, and more.
Related News
The Complexities of Governing Mental Health AI
Policymakers, academics, healthcare providers, AI developers, and patient advocates convened by Stanford HAI identify critical gaps in how we regulate AI tools used for therapy and emotional support.
Policymakers, academics, healthcare providers, AI developers, and patient advocates convened by Stanford HAI identify critical gaps in how we regulate AI tools used for therapy and emotional support.
Stanford researchers scanned 500 million words of state law to reveal the stunning growth of reporting requirements, and are now giving governments a tool to clean them up.
Stanford researchers scanned 500 million words of state law to reveal the stunning growth of reporting requirements, and are now giving governments a tool to clean them up.
Increasing reliance on language models for legal tasks have led to calls for more information about the likelihood and severity of potential errors. Realizing useful and robust transparency requires an institutional perspective, say the editors of a new special section in PNAS.
Increasing reliance on language models for legal tasks have led to calls for more information about the likelihood and severity of potential errors. Realizing useful and robust transparency requires an institutional perspective, say the editors of a new special section in PNAS.